SoroushVahidi/scidocs huggingface dataset:
Volumetric spatial transformer network for object recognition
(2016)

Understanding 3D environments is a vital element of modern computer vision research due to paramount relevance in many vision systems, spanning a wide field of application scenarios from self-driving cars to autonomous robots [Qi et al. 2016]. At the present time, object recognition mainly employs two methods: volumetric CNNs [Wu Z 2015] and multi-view CNNs [Xu et al. 2015] [Xu et al. 2016]. In this paper, we propose a volumetric spatial transformer network for object recognition. It fills the gap between 3D CNN and 2D CNN for the first time, and provides an end-to-end training fashion. Given a 3D shape, the network can automatically select the best view that maximizes the accuracy of object recognition.
Quranic Verse Extraction base on Concepts using OWL-DL Ontology
(2013)

In recent years, there has been a global growing demand for Islamic knowledge by both Muslims and non-Muslims. This has brought about a number of automated applications that ease the retrieval of knowledge from the Holy Book, being the major source of Knowledge in Islam. However, the current retrieval methods in the Quranic domain lack adequate semantic search capabilities; they are mostly based on the keywords matching approach. There is a lack of adequate linked data to provide a better description of concepts found in the Holy Quran. In this study we propose an Ontology assisted semantic search system in the Qur’an domain. The system makes use of Quran ontology and various relationships and restrictions. This will enable the user to semantically search for verses related to their query in Al-Quran. The system has improved the search capability of the Holy Quran knowledge to 95 percent accuracy level.
Outline for a Logical Theory of Adaptive Systems
(1962)

The purpose of this paper is to outline a theory of automata appropriate to the properties, requirements and questions of adaptation. The conditions that such a theory should satisfy come from not one but several fields: It should be possible to formulate, at least in an abstract version, some of the key hypotheses and problems from relevant parts of biology, particularly the areas concerned with molecular control and neurophysiology. The work in theoretical genetics initiated by R. A. Fisher [5] and Sewall Wright [24] should find a natural place in the theory. At the same time the rigorous methods of automata theory should be brought to bear (particularly those parts concerned with growing automata [1, 2, 3, 7, 8, 12, 15, 18, 23]). Finally the theory should include among its models abstract counterparts of artificial adaptive systems currently being studied, systems such as Newell-Shaw-Simon's "General Problem Solver" [13], Selfridge's "Pandemonium" [17], von Neumann's self-reproducing automata [22] and Turing's morphogenetic systems [19, 20]. The theory outlined here (which is intended as a theory and not the theory) is presented in four main parts. Section 2 discusses the study of adaptation via generation procedures and generated populations. Section 3 defines a continuum of generation procedures realizable in a reasonably direct fashion. Section 4 discusses the realization of generation procedures as populations of interacting programs in an iterative circuit computer. Section 5 discusses the process of adaptation in the context of the earlier sections. The paper concludes with a discussion of the nature of the theorems of this theory. Before entering upon the detailed discussion, one general feature of the theory should be noted. The interpretations or models of the theory divide into two broad categories: "complete" models and "incomplete" models. The "complete" models comprise the artificial systems--systems with properties and specifications completely delimited at the outset (cf. the rules of a game). One set of "complete" models for the theory consists of various programmed parallel computers. The "incomplete" models encompass natural systems. Any natural system involves an unlimited number of factors and, inevitably, the theory can handle only a selected few of these. Because there will always be variables which do not have explicit counterparts in the theory, the derived statements must be approximate relative to natural systems. For this reason it helps greatly that
Maximum Power Point Tracking Scheme for PV Systems Operating Under Partially Shaded Conditions
(2008)

Current-voltage and power-voltage characteristics of large photovoltaic (PV) arrays under partially shaded conditions are characterized by multiple steps and peaks. This makes the tracking of the actual maximum power point (MPP) [global peak (GP)] a difficult task. In addition, most of the existing schemes are unable to extract maximum power from the PV array under these conditions. This paper proposes a novel algorithm to track the global power peak under partially shaded conditions. The formulation of the algorithm is based on several critical observations made out of an extensive study of the PV characteristics and the behavior of the global and local peaks under partially shaded conditions. The proposed algorithm works in conjunction with a DC-DC converter to track the GP. In order to accelerate the tracking speed, a feedforward control scheme for operating the DC-DC converter is also proposed, which uses the reference voltage information from the tracking algorithm to shift the operation toward the MPP. The tracking time with this controller is about one-tenth as compared to a conventional controller. All the observations and conclusions, including simulation and experimental results, are presented.
A Particle Swarm Optimization-Based Maximum Power Point Tracking Algorithm for PV Systems Operating Under Partially Shaded Conditions
(2012)

A photovoltaic (PV) generation system (PGS) is becoming increasingly important as renewable energy sources due to its advantages such as absence of fuel cost, low maintenance requirement, and environmental friendliness. For large PGS, the probability for partially shaded condition (PSC) to occur is also high. Under PSC, the P-V curve of PGS exhibits multiple peaks, which reduces the effectiveness of conventional maximum power point tracking (MPPT) methods. In this paper, a particle swarm optimization (PSO)-based MPPT algorithm for PGS operating under PSC is proposed. The standard version of PSO is modified to meet the practical consideration of PGS operating under PSC. The problem formulation, design procedure, and parameter setting method which takes the hardware limitation into account are described and explained in detail. The proposed method boasts the advantages such as easy to implement, system-independent, and high tracking efficiency. To validate the correctness of the proposed method, simulation, and experimental results of a 500-W PGS will also be provided to demonstrate the effectiveness of the proposed technique.
Adolescent thriving: the role of sparks, relationships, and empowerment.
(2011)

Although most social science research on adolescence emphasizes risks and challenges, an emergent field of study focuses on adolescent thriving. The current study extends this line of inquiry by examining the additive power of identifying and nurturing young people's "sparks," giving them "voice," and providing the relationships and opportunities that reinforce and nourish thriving. A national sample of 1,817 adolescents, all age 15 (49% female), and including 56% white, 17% Hispanic/Latino, and 17% African-American adolescents, completed an online survey that investigated their deep passions or interests (their "sparks"), the opportunities and relationships they have to support pursuing those sparks, and how empowered they feel to make civic contributions (their "voice"). Results consistently supported the hypothesis that linking one's spark with a sense of voice and supportive opportunities and relationships strengthens concurrent outcomes, particularly those reflecting prosociality, during a key developmental transition period. The three developmental strengths also predicted most outcomes to a greater degree than did demographics. However, less than 10 percent of 15-year-olds reported experiencing high levels of all three strengths. The results demonstrate the value of focusing on thriving in adolescence, both to reframe our understanding of this age group and to highlight the urgency of providing adolescents the opportunities and relationships they need to thrive.
Large-Scale Multiclass Support Vector Machine Training via Euclidean Projection onto the Simplex
(2014)

Dual decomposition methods are the current state-of-the-art for training multiclass formulations of Support Vector Machines (SVMs). At every iteration, dual decomposition methods update a small subset of dual variables by solving a restricted optimization problem. In this paper, we propose an exact and efficient method for solving the restricted problem. In our method, the restricted problem is reduced to the well-known problem of Euclidean projection onto the positive simplex, which we can solve exactly in expected O(k) time, where k is the number of classes. We demonstrate that our method empirically achieves state-of-the-art convergence on several large-scale high-dimensional datasets.
The Foreign Language Effect on Moral Judgment: The Role of Emotions and Norms
(2015)

We investigated whether and why the use of a foreign language influences moral judgment. We studied the trolley and footbridge dilemmas, which propose an action that involves killing one individual to save five. In line with prior work, the use of a foreign language increased the endorsement of such consequentialist actions for the footbridge dilemma, but not for the trolley dilemma. But contrary to recent theorizing, this effect was not driven by an attenuation of emotions. An attenuation of emotions was found in both dilemmas, and it did not mediate the foreign language effect on moral judgment. An examination of additional scenarios revealed that foreign language influenced moral judgment when the proposed action involved a social or moral norm violation. We propose that foreign language influences moral judgment by reducing access to normative knowledge.
Confidence intervals for the overall effect size in random-effects meta-analysis.
(2008)

One of the main objectives in meta-analysis is to estimate the overall effect size by calculating a confidence interval (CI). The usual procedure consists of assuming a standard normal distribution and a sampling variance defined as the inverse of the sum of the estimated weights of the effect sizes. But this procedure does not take into account the uncertainty due to the fact that the heterogeneity variance (tau2) and the within-study variances have to be estimated, leading to CIs that are too narrow with the consequence that the actual coverage probability is smaller than the nominal confidence level. In this article, the performances of 3 alternatives to the standard CI procedure are examined under a random-effects model and 8 different tau2 estimators to estimate the weights: the t distribution CI, the weighted variance CI (with an improved variance), and the quantile approximation method (recently proposed). The results of a Monte Carlo simulation showed that the weighted variance CI outperformed the other methods regardless of the tau2 estimator, the value of tau2, the number of studies, and the sample size.
Automated leukemia detection in blood microscopic images using statistical texture analysis
(2011)

Pathological image analysis plays a significant role in effective disease diagnostics. Quantitative microscopy has supplemented clinicians with accurate results for diagnosis of dreaded diseases such as leukemia, hepatitis, AIDS, psoriasis. In this paper we present a texture based approach for automated leukemia detection. Acute lymphocytic leukemia (ALL) is a malignant disease characterized by the accumulation of lymphoblast in the bone marrow. Texture features of the blood nucleus are investigated for diagnostic prediction of ALL. Other shape features are also extracted to classify a lymphocytic cell in the blood image into normal lymphocyte or lymphoblast (blasts). Initial segmentation is done using K-means clustering which segregates leukocytes or white blood cells (WBC) from other blood components i.e. erythrocytes and platelets. The results of K-means are used for evaluating individual cell shape, texture and other features for final detection of leukemia. A total of 108 blood smear images were considered for feature extraction and final performance evaluation is validated with the results of a hematologist.
Self-Assembling Machine
(1994)

This letter describes dynamic self-assembly of two-component rotors floating at the interface between liquid and air into simple, reconfigurable mechanical systems ~‘‘machines’’!. The rotors are powered by an external, rotating magnetic field, and their positions within the interface are controlled by:~i! repulsive hydrodynamic interactions between them and ~ii ! by localized magnetic fields produced by an array of small electromagnets located below the plane of the interface. The mechanical functions of the machines depend on the spatiotemporal sequence of activation of the electromagnets. ©2004 American Institute of Physics. @DOI: 10.1063/1.1664019 #
Detection of cancer tumors in mammography images using support vector machine and mixed gravitational search algorithm
(2016)

In this paper, support vector machine (SVM) and mixed gravitational search algorithm (MGSA) are utilized to detect the breast cancer tumors in mammography images. Sech template matching method is used to segment images and extract the regions of interest (ROIs). Gray-level co-occurrence matrix (GLCM) is used to extract features. The mixed GSA is used for optimization of the classifier parameters and selecting salient features. The main goal of using MGSA-SVM is to decrease the number of features and to improve the SVM classification accuracy. Finally, the selected features and the tuned SVM classifier are used for detecting tumors. The experimental results show that the proposed method is able to optimize both feature selection and the SVM parameters for the breast cancer tumor detection.
Machine Learning for Vehicular Networks
(2017)

The emerging vehicular networks are expected to make everyday vehicular operation safer, greener, and more efficient, and pave the path to autonomous driving in the advent of the fifth generation (5G) cellular system. Machine learning, as a major branch of artificial intelligence, has been recently applied to wireless networks to provide a data-driven approach to solve traditionally challenging problems. In this article, we review recent advances in applying machine learning in vehicular networks and attempt to bring more attention to this emerging area. After a brief overview of the major concepts of machine learning, we present some application examples of machine learning in solving problems arising in vehicular networks. We finally discuss and highlight several open issues that warrant further research.
Mining Graphs for Understanding Time-Varying Volumetric Data
(2016)

A notable recent trend in time-varying volumetric data analysis and visualization is to extract data relationships and represent them in a low-dimensional abstract graph view for visual understanding and making connections to the underlying data. Nevertheless, the ever-growing size and complexity of data demands novel techniques that go beyond standard brushing and linking to allow significant reduction of cognition overhead and interaction cost. In this paper, we present a mining approach that automatically extracts meaningful features from a graph-based representation for exploring time-varying volumetric data. This is achieved through the utilization of a series of graph analysis techniques including graph simplification, community detection, and visual recommendation. We investigate the most important transition relationships for time-varying data and evaluate our solution with several time-varying data sets of different sizes and characteristics. For gaining insights from the data, we show that our solution is more efficient and effective than simply asking users to extract relationships via standard interaction techniques, especially when the data set is large and the relationships are complex. We also collect expert feedback to confirm the usefulness of our approach.
Human iris recognition in post-mortem subjects: Study and database
(2016)

This paper presents a unique study of post-mortem human iris recognition and the first known to us database of near-infrared and visible-light iris images of deceased humans collected up to almost 17 days after death. We used four different iris recognition methods to analyze the dynamics of iris quality decay in short-term comparisons (samples collected up to 60 hours after death) and long-term comparisons (for samples acquired up to 407 hours after demise). This study shows that post-mortem iris recognition is possible and occasionally works even 17 days after death. These conclusions contradict a promulgated rumor that iris is unusable shortly after decease. We make this dataset publicly available to let others verify our findings and to research new aspects of this important and unfamiliar topic. We are not aware of any earlier papers offering post-mortem human iris images and such comprehensive analysis employing four different matchers.
Performance analysis of visualmarkers for indoor navigation systems
(2016)

The massive diffusion of smartphones, the growing interest in wearable devices and the Internet of Things, and the exponential rise of location based services (LBSs) have made the problem of localization and navigation inside buildings one of the most important technological challenges of recent years. Indoor positioning systems have a huge market in the retail sector and contextual advertising; in addition, they can be fundamental to increasing the quality of life for citizens if deployed inside public buildings such as hospitals, airports, and museums. Sometimes, in emergency situations, they can make the difference between life and death. Various approaches have been proposed in the literature. Recently, thanks to the high performance of smartphones’ cameras, marker-less and marker-based computer vision approaches have been investigated. In a previous paper, we proposed a technique for indoor localization and navigation using both Bluetooth low energy (BLE) and a 2D visual marker system deployed into the floor. In this paper, we presented a qualitative performance evaluation of three 2D visual markers, Vuforia, ArUco marker, and AprilTag, which are suitable for real-time applications. Our analysis focused on specific case study of visual markers placed onto the tiles, to improve the efficiency of our indoor localization and navigation approach by choosing the best visual marker system.
An Investigation into Neural Net Optimization via Hessian Eigenvalue Density
(2019)

To understand the dynamics of optimization in deep neural networks, we develop a tool to study the evolution of the entire Hessian spectrum throughout the optimization process. Using this, we study a number of hypotheses concerning smoothness, curvature, and sharpness in the deep learning literature. We then thoroughly analyze a crucial structural feature of the spectra: in non-batch normalized networks, we observe the rapid appearance of large isolated eigenvalues in the spectrum, along with a surprising concentration of the gradient in the corresponding eigenspaces. In batch normalized networks, these two effects are almost absent. We characterize these effects, and explain how they affect optimization speed through both theory and experiments. As part of this work, we adapt advanced tools from numerical linear algebra that allow scalable and accurate estimation of the entire Hessian spectrum of ImageNet-scale neural networks; this technique may be of independent interest in other applications.
FlexiStickers: photogrammetric texture mapping using casual images
(2009)

Texturing 3D models using casual images has gained importance in the last decade, with the advent of huge databases of images. We present a novel approach for performing this task, which manages to account for the 3D geometry of the photographed object. Our method overcomes the limitation of both the constrained-parameterization approach, which does not account for the photography effects, and the photogrammetric approach, which cannot handle arbitrary images. The key idea of our algorithm is to formulate the mapping estimation as a Moving-Least-Squares problem for recovering local camera parameters at each vertex. The algorithm is realized in a FlexiStickers application, which enables fast interactive texture mapping using a small number of constraints.
Monte-Carlo Tree Search in Settlers of Catan
(2009)

Games are considered important benchmark tasks of artificial intelligence research. Modern strategic board games can typically be played by three or more people, which makes them suitable test beds for investigating multi-player strategic decision making. Monte-Carlo Tree Search (MCTS) is a recently published family of algorithms that achieved successful results with classical, two-player, perfect-information games such as Go. In this paper we apply MCTS to the multi-player, non-deterministic board game Settlers of Catan. We implemented an agent that is able to play against computer-controlled and human players. We show that MCTS can be adapted successfully to multi-agent environments, and present two approaches of providing the agent with a limited amount of domain knowledge. Our results show that the agent has considerable playing strength when compared to existing heuristics for the game. We conclude that MCTS is suitable for implementing a strong Settlers of Catan player.
Enduring effects for cognitive behavior therapy in the treatment of depression and anxiety.
(2006)

Recent studies suggest that cognitive and behavioral interventions have enduring effects that reduce risk for subsequent symptom return following treatment termination. These enduring effects have been most clearly demonstrated with respect to depression and the anxiety disorders. It remains unclear whether these effects are a consequence of the amelioration of the causal processes that generate risk or the introduction of compensatory strategies that offset them and whether these effects reflect the mobilization of cognitive or other mechanisms. No such enduring effects have been observed for the psychoactive medications, which appear to be largely palliative in nature. Other psychosocial interventions remain largely untested, although claims that they produce lasting change have long been made. Whether such enduring effects extend to other disorders remains to be seen, but the capacity to reduce risk following treatment termination is one of the major benefits provided by the cognitive and behavioral interventions with respect to the treatment of depression and the anxiety disorders.